fetchez 0.5.6

Geo-Spatial Data Fetching

Fetchez

Fetch geospatial data with ease.

Fetchez is a lightweight, modular, and highly extendable Python framework designed to orchestrate geospatial data engineering workflows.

Originally developed as the core fetching engine for the CUDEM project, Fetchez has evolved into a standalone ETL platform. It seamlessly retrieves Bathymetry, Topography, Imagery, and Oceanographic data from dozens of global repositories (NOAA, USGS, Copernicus, ESA) and processes it on the fly.

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❓ Why Fetchez?

Geospatial data engineering is traditionally fragmented. You often need one script to query an API, another tool to download the files, a GIS application to clip the data, and complex shell scripts to tie it all together.

Fetchez unifies the entire pipeline.

• Unified Interface: Access 50+ different modules using the exact same syntax.

• Parallel Fetching: High-performance, multi-threaded downloading with automatic retry, timeout handling, and partial-download resumption.

• Infrastructure as Code: Define complex data pipelines, cropping, and gridding workflows using CLI switches or simple YAML "Recipes".

• Pipeline Hooks: Transparently stream, filter, and process data (via globato and transformez) as it is being downloaded.

• Infinite Extensibility: Built on a modern plugin architecture. Drop custom Python scripts into a local folder, or install community extensions via pip to add your own data sources, domain schemas, processing hooks, etc.

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📦 Installation

pip install fetchez

🐄 Quickstart

Fetch Copernicus topography and NOAA multibeam bathymetry for a specific bounding box in one command:

CLI

fetchez -R loc:"Miami, FL" copernicus multibeam --audit-log miami_audit.json

Or run a full processing pipeline from a YAML recipe:

fetchez recipes/my_dem_project.yaml

Python

import fetchez

# Fetch Electronic Nautical Chart data from NOAA
files = fetchez.get("charts", region=[-120, -118, 33, 34], hooks=['unzip', 'filename_filter:match=.000', 'audit'])

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📚 Documentation

Would you like to know more? Check out our Official Documentation to learn about:

• Modules & Bundles: Discover and learn about data fetchers.

• The Python API: Build custom fetchers into your apps.

• Recipes & YAML: Run custom workflows from a simple YAML configuration.

• Hooks & Presets: Automate unzipping, filtering, and processing.

• Domain Schemas: Enforce rigorous geospatial standards automatically.

• Custom Plugins: Write your own data fetchers, processing hooks and extensions.

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⚖ License

This project is licensed under the MIT License - see the LICENSE file for details.

Copyright (c) 2010-2026 Regents of the University of Colorado